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Recently, graph neural networks (GNNs) have been successfully applied to recommender systems.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks , vol. 20, no. 1, pp. 61–80, 2008
2008
Earlier work this paper cites.
A. Mnih and R. R. Salakhutdinov, “Probabilistic matrix factorization,” in Annual Conference on Neural Information Processing Systems (NeurIPS) , 2008, pp. 1257–1264
2008
Earlier work this paper cites.
Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
Earlier work this paper cites.
S. Rendle, “Factorization machines,” in IEEE International Conference on Data Mining (ICDM) . IEEE, 2010, pp. 995–1000
2010
Earlier work this paper cites.
W. Pan, E. W. Xiang, N. N. Liu, and Q. Yang, “Transfer learning in collaborative filtering for sparsity reduction,” in AAAI Conference on Artificial Intelligence (AAAI) , 2010
2010
Earlier work this paper cites.
J. Tang, S. Wu, J. Sun, and H. Su, “Cross-domain collaboration recommendation,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) , 2012, pp. 1285–1293
2012
Earlier work this paper cites.
L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, and C. Zhu, “Personalized recommendation via cross-domain triadic factorization,” in International Conference on World Wide Web (WWW) . ACM, 2013, pp. 595–606
2013
Earlier work this paper cites.
S. Kabbur, X. Ning, and G. Karypis, “Fism: factored item similarity models for top-n recommender systems,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) , 2013, pp. 659–667
2013
Earlier work this paper cites.
B. Loni, Y. Shi, M. Larson, and A. Hanjalic, “Cross-domain collaborative filtering with factorization machines,” in European Conference on Information Retrieval (ECIR) . Springer, 2014, pp. 656–661
2014
Earlier work this paper cites.
Y.-F. Liu, C.-Y. Hsu, and S.-H. Wu, “Non-linear cross-domain collaborative filtering via hyper-structure transfer,” in International Conference on Machine Learning (ICML) , 2015, pp. 1190–1198
2015
Earlier work this paper cites.
W. Zhang, T. Du, and J. Wang, “Deep learning over multi-field categorical data,” in European Conference on Information Retrieval (ECIR) . Springer, 2016, pp. 45–57
2016
Earlier work this paper cites.
Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu, Y. Wen, and J. Wang, “Product-based neural networks for user response prediction,” in IEEE International Conference on Data Mining (ICDM) . IEEE, 2016, pp. 1149–1154
2016
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) , 2016, pp. 855–864
2016
Cited alongside, same era.
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in International Conference on World Wide Web (WWW) , 2017, pp. 173–182
2017
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Annual Conference on Neural Information Processing Systems (NeurIPS) , 2017, pp. 1024–1034
2017
Cited alongside, same era.
J. Ding, G. Yu, X. He, Y. Quan, Y. Li, T.-S. Chua, D. Jin, and J. Yu, “Improving implicit recommender systems with view data.” in International Joint Conference on Artificial Intelligence (IJCAI) , 2018, pp. 3343–3349
2018
Later among the works it cites.
X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural graph collaborative filtering,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) , 2019, pp. 165–174
2019
Later among the works it cites.
Z. Li, Z. Cui, S. Wu, X. Zhang, and L. Wang, “Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction,” in ACM International Conference on Information and Knowledge Management (CIKM) , 10 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
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X. He and T.-S. Chua, “Neural factorization machines for sparse predictive analytics,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) . ACM, 2017, pp. 355–364
2017
Cited alongside, same era.
H. Guo, R. Tang, Y. Ye, Z. Li, and X. He, “Deepfm: a factorization-machine based neural network for ctr prediction,” in International Joint Conference on Artificial Intelligence (IJCAI) , 2017, pp. 1725–1731
2017
Cited alongside, same era.
T. Man, H. Shen, X. Jin, and X. Cheng, “Cross-domain recommendation: An embedding and mapping approach.” in International Joint Conference on Artificial Intelligence (IJCAI) , 2017, pp. 2464–2470
2017
Cited alongside, same era.
R. Wang, B. Fu, G. Fu, and M. Wang, “Deep & cross network for ad click predictions,” in Proceedings of the ADKDD . ACM, 2017, p. 12
2017
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR) , 2015
2017
Cited alongside, same era.
P. Velićković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
J. Lian, X. Zhou, F. Zhang, Z. Chen, X. Xie, and G. Sun, “xdeepfm: Combining explicit and implicit feature interactions for recommender systems,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) . ACM, 2018, pp. 1754–1763
2018
Cited alongside, same era.
X. Wang, X. He, F. Feng, L. Nie, and T.-S. Chua, “Tem: Tree-enhanced embedding model for explainable recommendation,” in International Conference on World Wide Web (WWW) , 2018, pp. 1543–1552
2018
Cited alongside, same era.
C. Chen, M. Zhang, C. Wang, W. Ma, M. Li, Y. Liu, and S. Ma, “An efficient adaptive transfer neural network for social-aware recommendation,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) . ACM, 2019, pp. 225–234
2019
Later among the works it cites.
G. Hu, Y. Zhang, and Q. Yang, “Transfer meets hybrid: A synthetic approach for cross-domain collaborative filtering with text,” in International Conference on World Wide Web (WWW) , 2019, pp. 2822–2829
2019
Later among the works it cites.
F. Yuan, L. Yao, and B. Benatallah, “Darec: deep domain adaptation for cross-domain recommendation via transferring rating patterns,” in International Joint Conference on Artificial Intelligence (IJCAI) , 2019
2019
Later among the works it cites.
H. Yan, C. Yang, D. Yu, Y. Li, D. Jin, and D.-M. Chiu, “Multi-site user behavior modeling and its application in video recommendation,” IEEE Transactions on Knowledge and Data Engineering , 2019
2019
Later among the works it cites.
C. Gao, X. Chen, F. Feng, K. Zhao, X. He, Y. Li, and D. Jin, “Cross-domain recommendation without sharing user-relevant data,” in International Conference on World Wide Web (WWW) , 2019, pp. 491–502
2019
Later among the works it cites.
M. Ma, P. Ren, Y. Lin, Z. Chen, J. Ma, and M. d. Rijke, “ π \pi -net: A parallel information-sharing network for shared-account cross-domain sequential recommendations,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) , 2019, pp. 685–694
2019
Later among the works it cites.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in International Conference on Learning Representations (ICLR) , 2019
2019
Later among the works it cites.